Novel computer vision algorithm for the reliable analysis of organelle morphology in whole cell 3D images--A pilot study for the quantitative evaluation of mitochondrial fragmentation in amyotrophic lateral sclerosis.

Abstract The function of intact organelles, whether mitochondria, Golgi apparatus or endoplasmic reticulum (ER), relies on their proper morphological organization. It is recognized that disturbances of organelle morphology are early events in disease manifestation, but reliable and quantitative detection of organelle morphology is difficult and time-consuming. Here we present a novel computer vision algorithm for the assessment of organelle morphology in whole cell 3D images. The algorithm allows the numerical and quantitative description of organelle structures, including total number and length of segments, cell and nucleus area/volume as well as novel texture parameters like lacunarity and fractal dimension. Applying the algorithm we performed a pilot study in cultured motor neurons from transgenic G93A hSOD1 mice, a model of human familial amyotrophic lateral sclerosis. In the presence of the mutated SOD1 and upon excitotoxic treatment with kainate we demonstrate a clear fragmentation of the mitochondrial network, with an increase in the number of mitochondrial segments and a reduction in the length of mitochondria. Histogram analyses show a reduced number of tubular mitochondria and an increased number of small mitochondrial segments. The computer vision algorithm for the evaluation of organelle morphology allows an objective assessment of disease-related organelle phenotypes with greatly reduced examiner bias and will aid the evaluation of novel therapeutic strategies on a cellular level.